Data-Cleaning-and-ETL

DATA CLEANING AND ETL

Data Cleaning

Data cleaning is crucial to ensure the accuracy and reliability of our datasets. Here's a quick overview:

• Duplicate Removal: Eliminate redundant records.
• Handling Missing Values: Strategically manage and fill missing data.
• Format Standardization: Ensure consistency in data formats.
• Error Correction: Identify and rectify anomalies or outliers.

ETL Process

Our data pipeline follows the ETL paradigm, facilitating efficient data integration. Here's a breakdown:

Extract

Retrieve data from diverse sources, capturing relevant information for analysis.

Transform

Clean, structure, and enrich the data. Apply necessary transformations for meaningful insights.

Load

Transfer the transformed data to a target database, making it ready for reporting and analysis.